Linked Life Data

16708780

Source:http://linkedlifedata.com/resource/pubmed/id/16708780

Statements in which the resource exists as a subject.
PredicateObject
rdf:type
lifeskim:mentions
pubmed:issue
2
pubmed:dateCreated
2006-5-19
pubmed:abstractText
Missing data are a common problem in longitudinal studies in the health sciences. Motivated by data from the Muscatine Coronary Risk Factor (MCRF) study, a longitudinal study of obesity, we propose a simple imputation method for handling non-ignorable non-responses (i.e., when non-response is related to the specific values that should have been obtained) in longitudinal studies with either discrete or continuous outcomes. In the proposed approach, two regression models are specified; one for the marginal mean of the response, the other for the conditional mean of the response given non-response patterns. Statistical inference for the model parameters is based on the generalized estimating equations (GEE) approach. An appealing feature of the proposed method is that it can be readily implemented using existing, widely-available statistical software. The method is illustrated using longitudinal data on obesity from the MCRF study.
pubmed:grant
pubmed:language
eng
pubmed:journal
pubmed:citationSubset
IM
pubmed:status
MEDLINE
pubmed:month
Apr
pubmed:issn
0323-3847
pubmed:author
pubmed:issnType
Print
pubmed:volume
48
pubmed:owner
NLM
pubmed:authorsComplete
Y
pubmed:pagination
302-18
pubmed:dateRevised
2007-11-14
pubmed:meshHeading
pubmed:year
2006
pubmed:articleTitle
A simple imputation method for longitudinal studies with non-ignorable non-responses.
pubmed:affiliation
Department of Biostatistics, Harvard School of Public Health, Boston, MA 02115, USA. mwang@jimmy.harvard.edu
pubmed:publicationType
Journal Article, Research Support, N.I.H., Extramural